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Message-ID: <sqxh7H_00gNtAmTJ5Q@andrew.cmu.edu>
Date: Mon, 22 Mar 1999 18:27:15 -0500 (EST)
From: Erik Riedel <riedel+@CMU.EDU>
To: pgsql-hackers@postgreSQL.org
Subject: [HACKERS] optimizer and type question
Sender: owner-pgsql-hackers@postgreSQL.org
Precedence: bulk
Status: RO
[last week aggregation, this week, the optimizer]
I have a somewhat general optimizer question/problem that I would like
to get some input on - i.e. I'd like to know what is "supposed" to
work here and what I should be expecting. Sadly, I think the patch
for this is more involved than my last message.
Using my favorite table these days:
Table = lineitem
+------------------------+----------------------------------+-------+
| Field | Type | Length|
+------------------------+----------------------------------+-------+
| l_orderkey | int4 not null | 4 |
| l_partkey | int4 not null | 4 |
| l_suppkey | int4 not null | 4 |
| l_linenumber | int4 not null | 4 |
| l_quantity | float4 not null | 4 |
| l_extendedprice | float4 not null | 4 |
| l_discount | float4 not null | 4 |
| l_tax | float4 not null | 4 |
| l_returnflag | char() not null | 1 |
| l_linestatus | char() not null | 1 |
| l_shipdate | date | 4 |
| l_commitdate | date | 4 |
| l_receiptdate | date | 4 |
| l_shipinstruct | char() not null | 25 |
| l_shipmode | char() not null | 10 |
| l_comment | char() not null | 44 |
+------------------------+----------------------------------+-------+
Index: lineitem_index_
and the query:
--
-- Query 1
--
explain select l_returnflag, l_linestatus, sum(l_quantity) as sum_qty,
sum(l_extendedprice) as sum_base_price,
sum(l_extendedprice*(1-l_discount)) as sum_disc_price,
sum(l_extendedprice*(1-l_discount)*(1+l_tax)) as sum_charge,
avg(l_quantity) as avg_qty, avg(l_extendedprice) as avg_price,
avg(l_discount) as avg_disc, count(*) as count_order
from lineitem
where l_shipdate <= '1998-09-02'::date
group by l_returnflag, l_linestatus
order by l_returnflag, l_linestatus;
note that I have eliminated the date calculation in my query of last
week and manually replaced it with a constant (since this wasn't
happening automatically - but let's not worry about that for now).
And this is only an explain, we care about the optimizer. So we get:
Sort (cost=34467.88 size=0 width=0)
-> Aggregate (cost=34467.88 size=0 width=0)
-> Group (cost=34467.88 size=0 width=0)
-> Sort (cost=34467.88 size=0 width=0)
-> Seq Scan on lineitem (cost=34467.88 size=200191 width=44)
so let's think about the selectivity that is being chosen for the
seq scan (the where l_shipdate <= '1998-09-02').
Turns out the optimizer is choosing "33%", even though the real answer
is somewhere in 90+% (that's how the query is designed). So, why does
it do that?
Turns out that selectivity in this case is determined via
plancat::restriction_selectivity() which calls into functionOID = 103
(intltsel) for operatorOID = 1096 (date "<=") on relation OID = 18663
(my lineitem).
This all follows because of the description of 1096 (date "<=") in
pg_operator. Looking at local1_template1.bki.source near line 1754
shows:
insert OID = 1096 ( "<=" PGUID 0 <...> date_le intltsel intltjoinsel )
where we see that indeed, it thinks "intltsel" is the right function
to use for "oprrest" in the case of dates.
Question 1 - is intltsel the right thing for selectivity on dates?
Hope someone is still with me.
So now we're running selfuncs::intltsel() where we make a further call
to selfuncs::gethilokey(). The job of gethilokey is to determine the
min and max values of a particular attribute in the table, which will
then be used with the constant in my where clause to estimate the
selectivity. It is going to search the pg_statistic relation with
three key values:
Anum_pg_statistic_starelid 18663 (lineitem)
Anum_pg_statistic_staattnum 11 (l_shipdate)
Anum_pg_statistic_staop 1096 (date "<=")
this finds no tuples in pg_statistic. Why is that? The only nearby
tuple in pg_statistic is:
starelid|staattnum|staop|stalokey |stahikey
--------+---------+-----+----------------+----------------
18663| 11| 0|01-02-1992 |12-01-1998
and the reason the query doesn't match anything? Because 1096 != 0.
But why is it 0 in pg_statistic? Statistics are determined near line
1844 in vacuum.c (assuming a 'vacuum analyze' run at some point)
i = 0;
values[i++] = (Datum) relid; /* 1 */
values[i++] = (Datum) attp->attnum; /* 2 */
====> values[i++] = (Datum) InvalidOid; /* 3 */
fmgr_info(stats->outfunc, &out_function);
out_string = <...min...>
values[i++] = (Datum) fmgr(F_TEXTIN, out_string);
pfree(out_string);
out_string = <...max...>
values[i++] = (Datum) fmgr(F_TEXTIN, out_string);
pfree(out_string);
stup = heap_formtuple(sd->rd_att, values, nulls);
the "offending" line is setting the staop to InvalidOid (i.e. 0).
Question 2 - is this right? Is the intent for 0 to serve as a
"wildcard", or should it be inserting an entry for each operation
individually?
In the case of "wildcard" then gethilokey() should allow a match for
Anum_pg_statistic_staop 0
instead of requiring the more restrictive 1096. In the current code,
what happens next is gethilokey() returns "not found" and intltsel()
returns the default 1/3 which I see in the resultant query plan (size
= 200191 is 1/3 of the number of lineitem tuples).
Question 3 - is there any inherent reason it couldn't get this right?
The statistic is in the table 1992 to 1998, so the '1998-09-02' date
should be 90-some% selectivity, a much better guess than 33%.
Doesn't make a difference for this particular query, of course,
because the seq scan must proceed anyhow, but it could easily affect
other queries where selectivities matter (and it affects the
modifications I am trying to test in the optimizer to be "smarter"
about selectivities - my overall context is to understand/improve the
behavior that the underlying storage system sees from queries like this).
OK, so let's say we treat 0 as a "wildcard" and stop checking for
1096. Not we let gethilokey() return the two dates from the statistic
table. The immediate next thing that intltsel() does, near lines 122
in selfuncs.c is call atol() on the strings from gethilokey(). And
guess what it comes up with?
low = 1
high = 12
because it calls atol() on '01-02-1992' and '12-01-1998'. This
clearly isn't right, it should get some large integer that includes
the year and day in the result. Then it should compare reasonably
with my constant from the where clause and give a decent selectivity
value. This leads to a re-visit of Question 1.
Question 4 - should date "<=" use a dateltsel() function instead of
intltsel() as oprrest?
If anyone is still with me, could you tell me if this makes sense, or
if there is some other location where the appropriate type conversion
could take place so that intltsel() gets something reasonable when it
does the atol() calls?
Could someone also give me a sense for how far out-of-whack the whole
current selectivity-handling structure is? It seems that most of the
operators in pg_operator actually use intltsel() and would have
type-specific problems like that described. Or is the problem in the
way attribute values are stored in pg_statistic by vacuum analyze? Or
is there another layer where type conversion belongs?
Phew. Enough typing, hope someone can follow this and address at
least some of the questions.
Thanks.
Erik Riedel
Carnegie Mellon University
www.cs.cmu.edu/~riedel
From owner-pgsql-hackers@hub.org Mon Mar 22 20:31:11 1999
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To: Erik Riedel <riedel+@CMU.EDU>
cc: pgsql-hackers@postgreSQL.org
Subject: Re: [HACKERS] optimizer and type question
In-reply-to: Your message of Mon, 22 Mar 1999 18:27:15 -0500 (EST)
<sqxh7H_00gNtAmTJ5Q@andrew.cmu.edu>
Date: Mon, 22 Mar 1999 20:12:43 -0500
Message-ID: <23292.922151563@sss.pgh.pa.us>
From: Tom Lane <tgl@sss.pgh.pa.us>
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Precedence: bulk
Status: ROr
Erik Riedel <riedel+@CMU.EDU> writes:
> [ optimizer doesn't find relevant pg_statistic entry ]
It's clearly a bug that the selectivity code is not finding this tuple.
If your analysis is correct, then selectivity estimation has *never*
worked properly, or at least not in recent memory :-(. Yipes.
Bruce and I found a bunch of other problems in the optimizer recently,
so it doesn't faze me to assume that this is broken too.
> the "offending" line is setting the staop to InvalidOid (i.e. 0).
> Question 2 - is this right? Is the intent for 0 to serve as a
> "wildcard",
My thought is that what the staop column ought to be is the OID of the
comparison function that was used to determine the sort order of the
column. Without a sort op the lowest and highest keys in the column are
not well defined, so it makes no sense to assert "these are the lowest
and highest values" without providing the sort op that determined that.
(For sufficiently complex data types one could reasonably have multiple
ordering operators. A crude example is sorting on "circumference" and
"area" for polygons.) But typically the sort op will be the "<"
operator for the column data type.
So, the vacuum code is definitely broken --- it's not storing the sort
op that it used. The code in gethilokey might be broken too, depending
on how it is producing the operator it's trying to match against the
tuple. For example, if the actual operator in the query is any of
< <= > >= on int4, then int4lt ought to be used to probe the pg_statistic
table. I'm not sure if we have adequate info in pg_operator or pg_type
to let the optimizer code determine the right thing to probe with :-(
> The immediate next thing that intltsel() does, near lines 122
> in selfuncs.c is call atol() on the strings from gethilokey(). And
> guess what it comes up with?
> low = 1
> high = 12
> because it calls atol() on '01-02-1992' and '12-01-1998'. This
> clearly isn't right, it should get some large integer that includes
> the year and day in the result. Then it should compare reasonably
> with my constant from the where clause and give a decent selectivity
> value. This leads to a re-visit of Question 1.
> Question 4 - should date "<=" use a dateltsel() function instead of
> intltsel() as oprrest?
This is clearly busted as well. I'm not sure that creating dateltsel()
is the right fix, however, because if you go down that path then every
single datatype needs its own selectivity function; that's more than we
need.
What we really want here is to be able to map datatype values into
some sort of numeric range so that we can compute what fraction of the
low-key-to-high-key range is on each side of the probe value (the
constant taken from the query). This general concept will apply to
many scalar types, so what we want is a type-specific mapping function
and a less-specific fraction-computing-function. Offhand I'd say that
we want intltsel() and floatltsel(), plus conversion routines that can
produce either int4 or float8 from a data type as seems appropriate.
Anything that couldn't map to one or the other would have to supply its
own selectivity function.
> Or is the problem in the
> way attribute values are stored in pg_statistic by vacuum analyze?
Looks like it converts the low and high values to text and stores them
that way. Ugly as can be :-( but I'm not sure there is a good
alternative. We have no "wild card" column type AFAIK, which is what
these columns of pg_statistic would have to be to allow storage of
unconverted min and max values.
I think you've found a can of worms here. Congratulations ;-)
regards, tom lane
From owner-pgsql-hackers@hub.org Mon Mar 22 23:31:00 1999
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Date: Mon, 22 Mar 1999 23:14:55 -0500 (EST)
From: Erik Riedel <riedel+@CMU.EDU>
To: Tom Lane <tgl@sss.pgh.pa.us>
Subject: Re: [HACKERS] optimizer and type question
Cc: pgsql-hackers@postgreSQL.org
In-Reply-To: <23292.922151563@sss.pgh.pa.us>
References: <23292.922151563@sss.pgh.pa.us>
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Status: ROr
OK, building on your high-level explanation, I am attaching a patch that
attempts to do something "better" than the current code. Note that I
have only tested this with the date type and my particular query. I
haven't run it through the regression, so consider it "proof of concept"
at best. Although hopefully it will serve my purposes.
> My thought is that what the staop column ought to be is the OID of the
> comparison function that was used to determine the sort order of the
> column. Without a sort op the lowest and highest keys in the column are
> not well defined, so it makes no sense to assert "these are the lowest
> and highest values" without providing the sort op that determined that.
>
> (For sufficiently complex data types one could reasonably have multiple
> ordering operators. A crude example is sorting on "circumference" and
> "area" for polygons.) But typically the sort op will be the "<"
> operator for the column data type.
>
I changed vacuum.c to do exactly that. oid of the lt sort op.
> So, the vacuum code is definitely broken --- it's not storing the sort
> op that it used. The code in gethilokey might be broken too, depending
> on how it is producing the operator it's trying to match against the
> tuple. For example, if the actual operator in the query is any of
> < <= > >= on int4, then int4lt ought to be used to probe the pg_statistic
> table. I'm not sure if we have adequate info in pg_operator or pg_type
> to let the optimizer code determine the right thing to probe with :-(
>
This indeed seems like a bigger problem. I thought about somehow using
type-matching from the sort op and the actual operator in the query - if
both the left and right type match, then consider them the same for
purposes of this probe. That seemed complicated, so I punted in my
example - it just does the search with relid and attnum and assumes that
only returns one tuple. This works in my case (maybe in all cases,
because of the way vacuum is currently written - ?).
> What we really want here is to be able to map datatype values into
> some sort of numeric range so that we can compute what fraction of the
> low-key-to-high-key range is on each side of the probe value (the
> constant taken from the query). This general concept will apply to
> many scalar types, so what we want is a type-specific mapping function
> and a less-specific fraction-computing-function. Offhand I'd say that
> we want intltsel() and floatltsel(), plus conversion routines that can
> produce either int4 or float8 from a data type as seems appropriate.
> Anything that couldn't map to one or the other would have to supply its
> own selectivity function.
>
This is what my example then does. Uses the stored sort op to get the
type and then uses typinput to convert from the string to an int4.
Then puts the int4 back into string format because that's what everyone
was expecting.
It seems to work for my particular query. I now get:
(selfuncs) gethilokey() obj 18663 attr 11 opid 1096 (ignored)
(selfuncs) gethilokey() found op 1087 in pg_proc
(selfuncs) gethilokey() found type 1082 in pg_type
(selfuncs) gethilokey() going to use 1084 to convert type 1082
(selfuncs) gethilokey() have low -2921 high -396
(selfuncs) intltsel() high -396 low -2921 val -486
(plancat) restriction_selectivity() for func 103 op 1096 rel 18663 attr
11 const -486 flag 3 returns 0.964356
NOTICE: QUERY PLAN:
Sort (cost=34467.88 size=0 width=0)
-> Aggregate (cost=34467.88 size=0 width=0)
-> Group (cost=34467.88 size=0 width=0)
-> Sort (cost=34467.88 size=0 width=0)
-> Seq Scan on lineitem (cost=34467.88 size=579166 width=44)
including my printfs, which exist in the patch as well.
Selectivity is now the expected 96% and the size estimate for the seq
scan is much closer to correct.
Again, not tested with anything besides date, so caveat not-tested.
Hope this helps.
Erik
----------------------[optimizer_fix.sh]------------------------
#! /bin/sh
# This is a shell archive, meaning:
# 1. Remove everything above the #! /bin/sh line.
# 2. Save the resulting text in a file.
# 3. Execute the file with /bin/sh (not csh) to create:
# selfuncs.c.diff
# vacuum.c.diff
# This archive created: Mon Mar 22 22:58:14 1999
export PATH; PATH=/bin:/usr/bin:$PATH
if test -f 'selfuncs.c.diff'
then
echo shar: "will not over-write existing file 'selfuncs.c.diff'"
else
cat << \SHAR_EOF > 'selfuncs.c.diff'
***
/afs/ece.cmu.edu/project/lcs/lcs-004/er1p/postgres/611/src/backend/utils/adt
/selfuncs.c Thu Mar 11 23:59:35 1999
---
/afs/ece.cmu.edu/project/lcs/lcs-004/er1p/postgres/615/src/backend/utils/adt
/selfuncs.c Mon Mar 22 22:57:25 1999
***************
*** 32,37 ****
--- 32,40 ----
#include "utils/lsyscache.h" /* for get_oprrest() */
#include "catalog/pg_statistic.h"
+ #include "catalog/pg_proc.h" /* for Form_pg_proc */
+ #include "catalog/pg_type.h" /* for Form_pg_type */
+
/* N is not a valid var/constant or relation id */
#define NONVALUE(N) ((N) == -1)
***************
*** 103,110 ****
bottom;
result = (float64) palloc(sizeof(float64data));
! if (NONVALUE(attno) || NONVALUE(relid))
*result = 1.0 / 3;
else
{
/* XXX val = atol(value); */
--- 106,114 ----
bottom;
result = (float64) palloc(sizeof(float64data));
! if (NONVALUE(attno) || NONVALUE(relid)) {
*result = 1.0 / 3;
+ }
else
{
/* XXX val = atol(value); */
***************
*** 117,130 ****
}
high = atol(highchar);
low = atol(lowchar);
if ((flag & SEL_RIGHT && val < low) ||
(!(flag & SEL_RIGHT) && val > high))
{
float32data nvals;
nvals = getattdispersion(relid, (int) attno);
! if (nvals == 0)
*result = 1.0 / 3.0;
else
{
*result = 3.0 * (float64data) nvals;
--- 121,136 ----
}
high = atol(highchar);
low = atol(lowchar);
+ printf("(selfuncs) intltsel() high %d low %d val %d\n",high,low,val);
if ((flag & SEL_RIGHT && val < low) ||
(!(flag & SEL_RIGHT) && val > high))
{
float32data nvals;
nvals = getattdispersion(relid, (int) attno);
! if (nvals == 0) {
*result = 1.0 / 3.0;
+ }
else
{
*result = 3.0 * (float64data) nvals;
***************
*** 336,341 ****
--- 342,353 ----
{
Relation rel;
HeapScanDesc scan;
+ /* this assumes there is only one row in the statistics table for any
particular */
+ /* relid, attnum pair - could be more complicated if staop is also
used. */
+ /* at the moment, if there are multiple rows, this code ends up
picking the */
+ /* "first" one
- er1p */
+ /* the actual "ignoring" is done in the call to heap_beginscan()
below, where */
+ /* we only mention 2 of the 3 keys in this array
- er1p */
static ScanKeyData key[3] = {
{0, Anum_pg_statistic_starelid, F_OIDEQ, {0, 0, F_OIDEQ}},
{0, Anum_pg_statistic_staattnum, F_INT2EQ, {0, 0, F_INT2EQ}},
***************
*** 344,355 ****
bool isnull;
HeapTuple tuple;
rel = heap_openr(StatisticRelationName);
key[0].sk_argument = ObjectIdGetDatum(relid);
key[1].sk_argument = Int16GetDatum((int16) attnum);
key[2].sk_argument = ObjectIdGetDatum(opid);
! scan = heap_beginscan(rel, 0, SnapshotNow, 3, key);
tuple = heap_getnext(scan, 0);
if (!HeapTupleIsValid(tuple))
{
--- 356,377 ----
bool isnull;
HeapTuple tuple;
+ HeapTuple tup;
+ Form_pg_proc proc;
+ Form_pg_type typ;
+ Oid which_op;
+ Oid which_type;
+ int32 low_value;
+ int32 high_value;
+
rel = heap_openr(StatisticRelationName);
key[0].sk_argument = ObjectIdGetDatum(relid);
key[1].sk_argument = Int16GetDatum((int16) attnum);
key[2].sk_argument = ObjectIdGetDatum(opid);
! printf("(selfuncs) gethilokey() obj %d attr %d opid %d (ignored)\n",
! key[0].sk_argument,key[1].sk_argument,key[2].sk_argument);
! scan = heap_beginscan(rel, 0, SnapshotNow, 2, key);
tuple = heap_getnext(scan, 0);
if (!HeapTupleIsValid(tuple))
{
***************
*** 376,383 ****
--- 398,461 ----
&isnull));
if (isnull)
elog(DEBUG, "gethilokey: low key is null");
+
heap_endscan(scan);
heap_close(rel);
+
+ /* now we deal with type conversion issues
*/
+ /* when intltsel() calls this routine (who knows what other callers
might do) */
+ /* it assumes that it can call atol() on the strings and then use
integer */
+ /* comparison from there. what we are going to do here, then, is try
to use */
+ /* the type information from Anum_pg_statistic_staop to convert the
high */
+ /* and low values
- er1p */
+
+ /* WARNING: this code has only been tested with the date type and has
NOT */
+ /* been regression tested. consider it "sample" code of what might
be the */
+ /* right kind of thing to do
- er1p */
+
+ /* get the 'op' from pg_statistic and look it up in pg_proc */
+ which_op = heap_getattr(tuple,
+ Anum_pg_statistic_staop,
+ RelationGetDescr(rel),
+ &isnull);
+ if (InvalidOid == which_op) {
+ /* ignore all this stuff, try conversion only if we have a valid staop */
+ /* note that there is an accompanying change to 'vacuum analyze' that */
+ /* gets this set to something useful. */
+ } else {
+ /* staop looks valid, so let's see what we can do about conversion */
+ tup = SearchSysCacheTuple(PROOID, ObjectIdGetDatum(which_op), 0, 0, 0);
+ if (!HeapTupleIsValid(tup)) {
+ elog(ERROR, "selfuncs: unable to find op in pg_proc %d", which_op);
+ }
+ printf("(selfuncs) gethilokey() found op %d in pg_proc\n",which_op);
+
+ /* use that to determine the type of stahikey and stalokey via pg_type */
+ proc = (Form_pg_proc) GETSTRUCT(tup);
+ which_type = proc->proargtypes[0]; /* XXX - use left and right
separately? */
+ tup = SearchSysCacheTuple(TYPOID, ObjectIdGetDatum(which_type), 0, 0, 0);
+ if (!HeapTupleIsValid(tup)) {
+ elog(ERROR, "selfuncs: unable to find type in pg_type %d", which_type);
+ }
+ printf("(selfuncs) gethilokey() found type %d in pg_type\n",which_type);
+
+ /* and use that type to get the conversion function to int4 */
+ typ = (Form_pg_type) GETSTRUCT(tup);
+ printf("(selfuncs) gethilokey() going to use %d to convert type
%d\n",typ->typinput,which_type);
+
+ /* and convert the low and high strings */
+ low_value = (int32) fmgr(typ->typinput, *low, -1);
+ high_value = (int32) fmgr(typ->typinput, *high, -1);
+ printf("(selfuncs) gethilokey() have low %d high
%d\n",low_value,high_value);
+
+ /* now we have int4's, which we put back into strings because
that's what out */
+ /* callers (intltsel() at least) expect
- er1p */
+ pfree(*low); pfree(*high); /* let's not leak the old strings */
+ *low = int4out(low_value);
+ *high = int4out(high_value);
+
+ /* XXX - this probably leaks the two tups we got from
SearchSysCacheTuple() - er1p */
+ }
}
float64
SHAR_EOF
fi
if test -f 'vacuum.c.diff'
then
echo shar: "will not over-write existing file 'vacuum.c.diff'"
else
cat << \SHAR_EOF > 'vacuum.c.diff'
***
/afs/ece.cmu.edu/project/lcs/lcs-004/er1p/postgres/611/src/backend/commands/
vacuum.c Thu Mar 11 23:59:09 1999
---
/afs/ece.cmu.edu/project/lcs/lcs-004/er1p/postgres/615/src/backend/commands/
vacuum.c Mon Mar 22 21:23:15 1999
***************
*** 1842,1848 ****
i = 0;
values[i++] = (Datum) relid; /* 1 */
values[i++] = (Datum) attp->attnum; /* 2 */
! values[i++] = (Datum) InvalidOid; /* 3 */
fmgr_info(stats->outfunc, &out_function);
out_string = (*fmgr_faddr(&out_function)) (stats->min,
stats->attr->atttypid);
values[i++] = (Datum) fmgr(F_TEXTIN, out_string);
--- 1842,1848 ----
i = 0;
values[i++] = (Datum) relid; /* 1 */
values[i++] = (Datum) attp->attnum; /* 2 */
! values[i++] = (Datum) stats->f_cmplt.fn_oid; /* 3 */ /* get the
'<' oid, instead of 'invalid' - er1p */
fmgr_info(stats->outfunc, &out_function);
out_string = (*fmgr_faddr(&out_function)) (stats->min,
stats->attr->atttypid);
values[i++] = (Datum) fmgr(F_TEXTIN, out_string);
SHAR_EOF
fi
exit 0
# End of shell archive
From owner-pgsql-hackers@hub.org Tue Mar 23 12:31:05 1999
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Tue, 23 Mar 1999 12:01:57 -0500 (EST)
To: Erik Riedel <riedel+@CMU.EDU>
cc: pgsql-hackers@postgreSQL.org
Subject: Re: [HACKERS] optimizer and type question
In-reply-to: Your message of Mon, 22 Mar 1999 23:14:55 -0500 (EST)
<4qxlJ0200anI01hK40@andrew.cmu.edu>
Date: Tue, 23 Mar 1999 12:01:57 -0500
Message-ID: <24453.922208517@sss.pgh.pa.us>
From: Tom Lane <tgl@sss.pgh.pa.us>
Sender: owner-pgsql-hackers@postgreSQL.org
Precedence: bulk
Status: RO
Erik Riedel <riedel+@CMU.EDU> writes:
> OK, building on your high-level explanation, I am attaching a patch that
> attempts to do something "better" than the current code. Note that I
> have only tested this with the date type and my particular query.
Glad to see you working on this. I don't like the details of your
patch too much though ;-). Here are some suggestions for making it
better.
1. I think just removing staop from the lookup in gethilokey is OK for
now, though I'm dubious about Bruce's thought that we could delete that
field entirely. As you observe, vacuum will not currently put more
than one tuple for a column into pg_statistic, so we can just do the
lookup with relid and attno and leave it at that. But I think we ought
to leave the field there, with the idea that vacuum might someday
compute more than one statistic for a data column. Fixing vacuum to
put its sort op into the field is a good idea in the meantime.
2. The type conversion you're doing in gethilokey is a mess; I think
what you ought to make it do is simply the inbound conversion of the
string from pg_statistic into the internal representation for the
column's datatype, and return that value as a Datum. It also needs
a cleaner success/failure return convention --- this business with
"n" return is ridiculously type-specific. Also, the best and easiest
way to find the type to convert to is to look up the column type in
the info for the given relid, not search pg_proc with the staop value.
(I'm not sure that will even work, since there are pg_proc entries
with wildcard argument types.)
3. The atol() calls currently found in intltsel are a type-specific
cheat on what is conceptually a two-step process:
* Convert the string stored in pg_statistic back to the internal
form for the column data type.
* Generate a numeric representation of the data value that can be
used as an estimate of the range of values in the table.
The second step is trivial for integers, which may obscure the fact
that there are two steps involved, but nonetheless there are. If
you think about applying selectivity logic to strings, say, it
becomes clear that the second step is a necessary component of the
process. Furthermore, the second step must also be applied to the
probe value that's being passed into the selectivity operator.
(The probe value is already in internal form, of course; but it is
not necessarily in a useful numeric form.)
We can do the first of these steps by applying the appropriate "XXXin"
conversion function for the column data type, as you have done. The
interesting question is how to do the second one. A really clean
solution would require adding a column to pg_type that points to a
function that will do the appropriate conversion. I'd be inclined to
make all of these functions return "double" (float8) and just have one
top-level selectivity routine for all data types that can use
range-based selectivity logic.
We could probably hack something together that would not use an explicit
conversion function for each data type, but instead would rely on
type-specific assumptions inside the selectivity routines. We'd need many
more selectivity routines though (at least one for each of int, float4,
float8, and text data types) so I'm not sure we'd really save any work
compared to doing it right.
BTW, now that I look at this issue it's real clear that the selectivity
entries in pg_operator are horribly broken. The intltsel/intgtsel
selectivity routines are currently applied to 32 distinct data types:
regression=> select distinct typname,oprleft from pg_operator, pg_type
regression-> where pg_type.oid = oprleft
regression-> and oprrest in (103,104);
typname |oprleft
---------+-------
_aclitem | 1034
abstime | 702
bool | 16
box | 603
bpchar | 1042
char | 18
cidr | 650
circle | 718
date | 1082
datetime | 1184
float4 | 700
float8 | 701
inet | 869
int2 | 21
int4 | 23
int8 | 20
line | 628
lseg | 601
macaddr | 829
money | 790
name | 19
numeric | 1700
oid | 26
oid8 | 30
path | 602
point | 600
polygon | 604
text | 25
time | 1083
timespan | 1186
timestamp| 1296
varchar | 1043
(32 rows)
many of which are very obviously not compatible with integer for *any*
purpose. It looks to me like a lot of data types were added to
pg_operator just by copy-and-paste, without paying attention to whether
the selectivity routines were actually correct for the data type.
As the code stands today, the bogus entries don't matter because
gethilokey always fails, so we always get 1/3 as the selectivity
estimate for any comparison operator (except = and != of course).
I had actually noticed that fact and assumed that it was supposed
to work that way :-(. But, clearly, there is code in here that
is *trying* to be smarter.
As soon as we fix gethilokey so that it can succeed, we will start
getting essentially-random selectivity estimates for those data types
that aren't actually binary-compatible with integer. That will not do;
we have to do something about the issue.
regards, tom lane
From tgl@sss.pgh.pa.us Tue Mar 23 12:31:02 1999
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To: Bruce Momjian <maillist@candle.pha.pa.us>
cc: riedel+@CMU.EDU, pgsql-hackers@postgreSQL.org
Subject: Re: [HACKERS] optimizer and type question
In-reply-to: Your message of Mon, 22 Mar 1999 21:25:45 -0500 (EST)
<199903230225.VAA01641@candle.pha.pa.us>
Date: Tue, 23 Mar 1999 12:09:52 -0500
Message-ID: <24471.922208992@sss.pgh.pa.us>
From: Tom Lane <tgl@sss.pgh.pa.us>
Status: RO
Bruce Momjian <maillist@candle.pha.pa.us> writes:
> What we really need is some way to determine how far the requested value
> is from the min/max values. With int, we just do (val-min)/(max-min).
> That works, but how do we do that for types that don't support division.
> Strings come to mind in this case.
What I'm envisioning is that we still apply the (val-min)/(max-min)
logic, but apply it to numeric values that are produced in a
type-dependent way.
For ints and floats the conversion is trivial, of course.
For strings, the first thing that comes to mind is to return 0 for a
null string and the value of the first byte for a non-null string.
This would give you one-part-in-256 selectivity which is plenty good
enough for what the selectivity code needs to do. (Actually, it's
only that good if the strings' first bytes are pretty well spread out.
If you have a table containing English words, for example, you might
only get about one part in 26 this way, since the first bytes will
probably only run from A to Z. Might be better to use the first two
characters of the string to compute the selectivity representation.)
In general, you can apply this logic as long as you can come up with
some numerical approximation to the data type's sorting order. It
doesn't have to be exact.
regards, tom lane
From owner-pgsql-hackers@hub.org Tue Mar 23 12:31:03 1999
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To: Bruce Momjian <maillist@candle.pha.pa.us>
cc: riedel+@CMU.EDU, pgsql-hackers@postgreSQL.org
Subject: Re: [HACKERS] optimizer and type question
In-reply-to: Your message of Mon, 22 Mar 1999 21:25:45 -0500 (EST)
<199903230225.VAA01641@candle.pha.pa.us>
Date: Tue, 23 Mar 1999 12:09:52 -0500
Message-ID: <24471.922208992@sss.pgh.pa.us>
From: Tom Lane <tgl@sss.pgh.pa.us>
Sender: owner-pgsql-hackers@postgreSQL.org
Precedence: bulk
Status: RO
Bruce Momjian <maillist@candle.pha.pa.us> writes:
> What we really need is some way to determine how far the requested value
> is from the min/max values. With int, we just do (val-min)/(max-min).
> That works, but how do we do that for types that don't support division.
> Strings come to mind in this case.
What I'm envisioning is that we still apply the (val-min)/(max-min)
logic, but apply it to numeric values that are produced in a
type-dependent way.
For ints and floats the conversion is trivial, of course.
For strings, the first thing that comes to mind is to return 0 for a
null string and the value of the first byte for a non-null string.
This would give you one-part-in-256 selectivity which is plenty good
enough for what the selectivity code needs to do. (Actually, it's
only that good if the strings' first bytes are pretty well spread out.
If you have a table containing English words, for example, you might
only get about one part in 26 this way, since the first bytes will
probably only run from A to Z. Might be better to use the first two
characters of the string to compute the selectivity representation.)
In general, you can apply this logic as long as you can come up with
some numerical approximation to the data type's sorting order. It
doesn't have to be exact.
regards, tom lane
From owner-pgsql-hackers@hub.org Thu Jul 1 20:39:19 1999
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From: "Hiroshi Inoue" <Inoue@tpf.co.jp>
To: "pgsql-hackers" <pgsql-hackers@postgreSQL.org>
Subject: [HACKERS] Optimization FAQ ?
Date: Fri, 2 Jul 1999 09:14:10 +0900
Message-ID: <000401bec41f$ce81dcc0$2801007e@cadzone.tpf.co.jp>
MIME-Version: 1.0
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charset="iso-2022-jp"
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Status: RO
Hello all,
I got the following result.
It's FAQ ?
drop table int2t;
create table int2t (id int2 primary key);
explain select * from int2t where id=1;
NOTICE: QUERY PLAN:
Seq Scan on int2t (cost=43.00 rows=2 width=2)
explain select * from int2t where id=1::int2;
NOTICE: QUERY PLAN:
Index Scan using int2t_pkey on int2t (cost=2.05 rows=2 width=2)
explain select * from int2t where id='1';
NOTICE: QUERY PLAN:
Index Scan using int2t_pkey on int2t (cost=2.05 rows=2 width=2)
Right behavior ?
Regards.
Hiroshi Inoue
Inoue@tpf.co.jp
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To: pgsql-hackers@postgreSQL.org
Subject: [HACKERS] Some notes on optimizer cost estimates
Date: Thu, 20 Jan 2000 19:31:32 -0500
Message-ID: <25387.948414692@sss.pgh.pa.us>
From: Tom Lane <tgl@sss.pgh.pa.us>
Sender: owner-pgsql-hackers@postgreSQL.org
Status: OR
I have been spending some time measuring actual runtimes for various
sequential-scan and index-scan query plans, and have learned that the
current Postgres optimizer's cost estimation equations are not very
close to reality at all.
Presently we estimate the cost of a sequential scan as
Nblocks + CPU_PAGE_WEIGHT * Ntuples
--- that is, the unit of cost is the time to read one disk page,
and we have a "fudge factor" that relates CPU time per tuple to
disk time per page. (The default CPU_PAGE_WEIGHT is 0.033, which
is probably too high for modern hardware --- 0.01 seems like it
might be a better default, at least for simple queries.) OK,
it's a simplistic model, but not too unreasonable so far.
The cost of an index scan is measured in these same terms as
Nblocks + CPU_PAGE_WEIGHT * Ntuples +
CPU_INDEX_PAGE_WEIGHT * Nindextuples
Here Ntuples is the number of tuples selected by the index qual
condition (typically, it's less than the total table size used in
sequential-scan estimation). CPU_INDEX_PAGE_WEIGHT essentially
estimates the cost of scanning an index tuple; by default it's 0.017 or
half CPU_PAGE_WEIGHT. Nblocks is estimated as the index size plus an
appropriate fraction of the main table size.
There are two big problems with this:
1. Since main-table tuples are visited in index order, we'll be hopping
around from page to page in the table. The current cost estimation
method essentially assumes that the buffer cache plus OS disk cache will
be 100% efficient --- we will never have to read the same page of the
main table twice in a scan, due to having discarded it between
references. This of course is unreasonably optimistic. Worst case
is that we'd fetch a main-table page for each selected tuple, but in
most cases that'd be unreasonably pessimistic.
2. The cost of a disk page fetch is estimated at 1.0 unit for both
sequential and index scans. In reality, sequential access is *much*
cheaper than the quasi-random accesses performed by an index scan.
This is partly a matter of physical disk seeks, and partly a matter
of benefitting (or not) from any read-ahead logic the OS may employ.
As best I can measure on my hardware, the cost of a nonsequential
disk read should be estimated at 4 to 5 times the cost of a sequential
one --- I'm getting numbers like 2.2 msec per disk page for sequential
scans, and as much as 11 msec per page for index scans. I don't
know, however, if this ratio is similar enough on other platforms
to be useful for cost estimating. We could make it a parameter like
we do for CPU_PAGE_WEIGHT ... but you know and I know that no one
ever bothers to adjust those numbers in the field ...
The other effect that needs to be modeled, and currently is not, is the
"hit rate" of buffer cache. Presumably, this is 100% for tables smaller
than the cache and drops off as the table size increases --- but I have
no particular thoughts on the form of the dependency. Does anyone have
ideas here? The problem is complicated by the fact that we don't really
know how big the cache is; we know the number of buffers Postgres has,
but we have no idea how big a disk cache the kernel is keeping. As near
as I can tell, finding a hit in the kernel disk cache is not a lot more
expensive than having the page sitting in Postgres' own buffers ---
certainly it's much much cheaper than a disk read.
BTW, if you want to do some measurements of your own, try turning on
PGOPTIONS="-d 2 -te". This will dump a lot of interesting numbers
into the postmaster log, if your platform supports getrusage().
regards, tom lane
************
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To: pgsql-hackers@postgreSQL.org
Reply-to: xun@cs.ucsb.edu
Subject: Re. [HACKERS] Some notes on optimizer cost estimates
Date: Thu, 20 Jan 2000 18:19:40 -0800
From: Xun Cheng <xun@cs.ucsb.edu>
Sender: owner-pgsql-hackers@postgreSQL.org
Status: OR
I'm very glad you bring up this cost estimate issue.
Recent work in database research have argued a more
detailed disk access cost model should be used for
large queries especially joins.
Traditional cost estimate only considers the number of
disk pages accessed. However a more detailed model
would consider three parameters: avg. seek, avg. latency
and avg. page transfer. For old disk, typical values are
SEEK=9.5 milliseconds, LATENCY=8.3 ms, TRANSFER=2.6ms.
A sequential continuous reading of a table (assuming
1000 continuous pages) would cost
(SEEK+LATENCY+1000*TRANFER=2617.8ms); while quasi-randomly
reading 200 times with 2 continuous pages/time would
cost (SEEK+200*LATENCY+400*TRANSFER=2700ms).
Someone from IBM lab re-studied the traditional
ad hoc join algorithms (nested, sort-merge, hash) using the detailed cost model
and found some interesting results.
>I have been spending some time measuring actual runtimes for various
>sequential-scan and index-scan query plans, and have learned that the
>current Postgres optimizer's cost estimation equations are not very
>close to reality at all.
One interesting question I'd like to ask is if this non-closeness
really affects the optimal choice of postgresql's query optimizer.
And to what degree the effects might be? My point is that
if the optimizer estimated the cost for sequential-scan is 10 and
the cost for index-scan is 20 while the actual costs are 10 vs. 40,
it should be ok because the optimizer would still choose sequential-scan
as it should.
>1. Since main-table tuples are visited in index order, we'll be hopping
>around from page to page in the table.
I'm not sure about the implementation in postgresql. One thing you might
be able to do is to first collect all must-read page addresses from
the index scan and then order them before the actual ordered page fetching.
It would at least avoid the same page being read twice (not entirely
true depending on the context (like in join) and algo.)
>The current cost estimation
>method essentially assumes that the buffer cache plus OS disk cache will
>be 100% efficient --- we will never have to read the same page of the
>main table twice in a scan, due to having discarded it between
>references. This of course is unreasonably optimistic. Worst case
>is that we'd fetch a main-table page for each selected tuple, but in
>most cases that'd be unreasonably pessimistic.
This is actually the motivation that I asked before if postgresql
has a raw disk facility. That way we have much control on this cache
issue. Of course only if we can provide some algo. better than OS
cache algo. (depending on the context, like large joins), a raw disk
facility will be worthwhile (besides the recoverability).
Actually I have another question for you guys which is somehow related
to this cost estimation issue. You know the difference between OLTP
and OLAP. My question is how you target postgresql on both kinds
of applications or just OLTP. From what I know OLTP and OLAP would
have a big difference in query characteristics and thus
optimization difference. If postgresql is only targeted on
OLTP, the above cost estimation issue might not be that
important. However for OLAP, large tables and large queries are
common and optimization would be difficult.
xun
************
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To: "Hiroshi Inoue" <Inoue@tpf.co.jp>
cc: pgsql-hackers@postgreSQL.org
Subject: Re: [HACKERS] Some notes on optimizer cost estimates
In-reply-to: <000b01bf63b1$093cbd40$2801007e@tpf.co.jp>
References: <000b01bf63b1$093cbd40$2801007e@tpf.co.jp>
Comments: In-reply-to "Hiroshi Inoue" <Inoue@tpf.co.jp>
message dated "Fri, 21 Jan 2000 10:44:20 +0900"
Date: Thu, 20 Jan 2000 21:30:41 -0500
Message-ID: <26758.948421841@sss.pgh.pa.us>
From: Tom Lane <tgl@sss.pgh.pa.us>
Sender: owner-pgsql-hackers@postgreSQL.org
Status: ORr
"Hiroshi Inoue" <Inoue@tpf.co.jp> writes:
> I've wondered why we cound't analyze database without vacuum.
> We couldn't run vacuum light-heartedly because it acquires an
> exclusive lock for the target table.
There is probably no real good reason, except backwards compatibility,
why the ANALYZE function (obtaining pg_statistic data) is part of
VACUUM at all --- it could just as easily be a separate command that
would only use read access on the database. Bruce is thinking about
restructuring VACUUM, so maybe now is a good time to think about
splitting out the ANALYZE code too.
> In addition,vacuum error occurs with analyze option in most
> cases AFAIK.
Still, with current sources? What's the error message? I fixed
a problem with pg_statistic tuples getting too big...
regards, tom lane
************
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To: Bruce Momjian <pgman@candle.pha.pa.us>
cc: Hiroshi Inoue <Inoue@tpf.co.jp>, pgsql-hackers@postgresql.org
Subject: Re: [HACKERS] Some notes on optimizer cost estimates
In-reply-to: <200001210248.VAA07186@candle.pha.pa.us>
References: <200001210248.VAA07186@candle.pha.pa.us>
Comments: In-reply-to Bruce Momjian <pgman@candle.pha.pa.us>
message dated "Thu, 20 Jan 2000 21:48:57 -0500"
Date: Thu, 20 Jan 2000 22:10:28 -0500
Message-ID: <27077.948424228@sss.pgh.pa.us>
From: Tom Lane <tgl@sss.pgh.pa.us>
Status: OR
Bruce Momjian <pgman@candle.pha.pa.us> writes:
> It is nice that ANALYZE is done during vacuum. I can't imagine why you
> would want to do an analyze without adding a vacuum to it. I guess
> that's why I made them the same command.
Well, the main bad thing about ANALYZE being part of VACUUM is that
it adds to the length of time that VACUUM is holding an exclusive
lock on the table. I think it'd make more sense for it to be a
separate command.
I have also been thinking about how to make ANALYZE produce a more
reliable estimate of the most common value. The three-element list
that it keeps now is a good low-cost hack, but it really doesn't
produce a trustworthy answer unless the MCV is pretty darn C (since
it will never pick up on the MCV at all until there are at least
two occurrences in three adjacent tuples). The only idea I've come
up with is to use a larger list, which would be slower and take
more memory. I think that'd be OK in a separate command, but I
hesitate to do it inside VACUUM --- VACUUM has its own considerable
memory requirements, and there's still the issue of not holding down
an exclusive lock longer than you have to.
regards, tom lane
From Inoue@tpf.co.jp Thu Jan 20 21:08:32 2000
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From: "Hiroshi Inoue" <Inoue@tpf.co.jp>
To: "Bruce Momjian" <pgman@candle.pha.pa.us>, "Tom Lane" <tgl@sss.pgh.pa.us>
Cc: <pgsql-hackers@postgreSQL.org>
Subject: RE: [HACKERS] Some notes on optimizer cost estimates
Date: Fri, 21 Jan 2000 12:14:10 +0900
Message-ID: <001301bf63bd$95cbe680$2801007e@tpf.co.jp>
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Status: OR
> -----Original Message-----
> From: Bruce Momjian [mailto:pgman@candle.pha.pa.us]
>
> > "Hiroshi Inoue" <Inoue@tpf.co.jp> writes:
> > > I've wondered why we cound't analyze database without vacuum.
> > > We couldn't run vacuum light-heartedly because it acquires an
> > > exclusive lock for the target table.
> >
> > There is probably no real good reason, except backwards compatibility,
> > why the ANALYZE function (obtaining pg_statistic data) is part of
> > VACUUM at all --- it could just as easily be a separate command that
> > would only use read access on the database. Bruce is thinking about
> > restructuring VACUUM, so maybe now is a good time to think about
> > splitting out the ANALYZE code too.
>
> I put it in vacuum because at the time I didn't know how to do such
> things and vacuum already scanned the table. I just linked on the the
> scan. Seemed like a good idea at the time.
>
> It is nice that ANALYZE is done during vacuum. I can't imagine why you
> would want to do an analyze without adding a vacuum to it. I guess
> that's why I made them the same command.
>
> If I made them separate commands, both would have to scan the table,
> though the analyze could do it without the exclusive lock, which would
> be good.
>
The functionality of VACUUM and ANALYZE is quite different.
I don't prefer to charge VACUUM more than now about analyzing
database. Probably looong lock,more aborts ....
Various kind of analysis would be possible by splitting out ANALYZE.
Regards.
Hiroshi Inoue
Inoue@tpf.co.jp
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Date: Fri, 21 Jan 2000 08:10:44 -0800
To: xun@cs.ucsb.edu, pgsql-hackers@postgreSQL.org
From: Don Baccus <dhogaza@pacifier.com>
Subject: Re: Re. [HACKERS] Some notes on optimizer cost estimates
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Status: OR
At 06:19 PM 1/20/00 -0800, Xun Cheng wrote:
>I'm very glad you bring up this cost estimate issue.
>Recent work in database research have argued a more
>detailed disk access cost model should be used for
>large queries especially joins.
>Traditional cost estimate only considers the number of
>disk pages accessed. However a more detailed model
>would consider three parameters: avg. seek, avg. latency
>and avg. page transfer. For old disk, typical values are
>SEEK=9.5 milliseconds, LATENCY=8.3 ms, TRANSFER=2.6ms.
>A sequential continuous reading of a table (assuming
>1000 continuous pages) would cost
>(SEEK+LATENCY+1000*TRANFER=2617.8ms); while quasi-randomly
>reading 200 times with 2 continuous pages/time would
>cost (SEEK+200*LATENCY+400*TRANSFER=2700ms).
>Someone from IBM lab re-studied the traditional
>ad hoc join algorithms (nested, sort-merge, hash) using the detailed cost
model
>and found some interesting results.
One complication when doing an index scan is that you are
accessing two separate files (table and index), which can frequently
be expected to cause an considerable increase in average seek time.
Oracle and other commercial databases recommend spreading indices and
tables over several spindles if at all possible in order to minimize
this effect.
I suspect it also helps their optimizer make decisions that are
more consistently good for customers with the largest and most
complex databases and queries, by making cost estimates more predictably
reasonable.
Still...this doesn't help with the question about the effect of the
filesystem system cache. I wandered around the web for a little bit
last night, and found one summary of a paper by Osterhout on the
effect of the Solaris cache on a fileserver serving diskless workstations.
There was reference to the hierarchy involved (i.e. the local workstation
cache is faster than the fileserver's cache which has to be read via
the network which in turn is faster than reading from the fileserver's
disk). It appears the rule-of-thumb for the cache-hit ratio on reads,
presumably based on measuring some internal Sun systems, used in their
calculations was 80%.
Just a datapoint to think about.
There's also considerable operating system theory on paging systems
that might be useful for thinking about trying to estimate the
Postgres cache/hit ratio. Then again, maybe Postgres could just
keep count of how many pages of a given table are in the cache at
any given time? Or simply keep track of the current ratio of hits
and misses?
>>I have been spending some time measuring actual runtimes for various
>>sequential-scan and index-scan query plans, and have learned that the
>>current Postgres optimizer's cost estimation equations are not very
>>close to reality at all.
>One interesting question I'd like to ask is if this non-closeness
>really affects the optimal choice of postgresql's query optimizer.
>And to what degree the effects might be? My point is that
>if the optimizer estimated the cost for sequential-scan is 10 and
>the cost for index-scan is 20 while the actual costs are 10 vs. 40,
>it should be ok because the optimizer would still choose sequential-scan
>as it should.
This is crucial, of course - if there are only two types of scans
available, what ever heuristic is used only has to be accurate enough
to pick the right one. Once the choice is made, it doesn't really
matter (from the optimizer's POV) just how long it will actually take,
the time will be spent and presumably it will be shorter than the
alternative.
How frequently will the optimizer choose wrongly if:
1. All of the tables and indices were in PG buffer cache or filesystem
cache? (i.e. fixed access times for both types of scans)
or
2. The table's so big that only a small fraction can reside in RAM
during the scan and join, which means that the non-sequential
disk access pattern of the indexed scan is much more expensive.
Also, if you pick sequential scans more frequently based on a presumption
that index scans are expensive due to increased average seek time, how
often will this penalize the heavy-duty user that invests in extra
drives and lots of RAM?
...
>>The current cost estimation
>>method essentially assumes that the buffer cache plus OS disk cache will
>>be 100% efficient --- we will never have to read the same page of the
>>main table twice in a scan, due to having discarded it between
>>references. This of course is unreasonably optimistic. Worst case
>>is that we'd fetch a main-table page for each selected tuple, but in
>>most cases that'd be unreasonably pessimistic.
>
>This is actually the motivation that I asked before if postgresql
>has a raw disk facility. That way we have much control on this cache
>issue. Of course only if we can provide some algo. better than OS
>cache algo. (depending on the context, like large joins), a raw disk
>facility will be worthwhile (besides the recoverability).
Postgres does have control over its buffer cache. The one thing that
raw disk I/O would give you is control over where blocks are placed,
meaning you could more accurately model the cost of retrieving them.
So presumably the cache could be tuned to the allocation algorithm
used to place various structures on the disk.
I still wonder just how much gain you get by this approach. Compared,
to, say simply spending $2,000 on a gigabyte of RAM. Heck, PCs even
support a couple gigs of RAM now.
>Actually I have another question for you guys which is somehow related
>to this cost estimation issue. You know the difference between OLTP
>and OLAP. My question is how you target postgresql on both kinds
>of applications or just OLTP. From what I know OLTP and OLAP would
>have a big difference in query characteristics and thus
>optimization difference. If postgresql is only targeted on
>OLTP, the above cost estimation issue might not be that
>important. However for OLAP, large tables and large queries are
>common and optimization would be difficult.
- Don Baccus, Portland OR <dhogaza@pacifier.com>
Nature photos, on-line guides, Pacific Northwest
Rare Bird Alert Service and other goodies at
http://donb.photo.net.
************
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Date: Mon, 21 Aug 2000 16:48:08 +0100 (WEST)
From: =?iso-8859-1?Q?Tiago_Ant=E3o?= <tra@fct.unl.pt>
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To: Tom Lane <tgl@sss.pgh.pa.us>
cc: pgsql-hackers@postgresql.org
Subject: Re: [HACKERS] Optimisation deficiency: currval('seq')-->seq scan,
constant-->index scan
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On Mon, 21 Aug 2000, Tom Lane wrote:
> > One thing it might be interesting (please tell me if you think
> > otherwise) would be to improve pg with better statistical information, by
> > using, for example, histograms.
>
> Yes, that's been on the todo list for a while.
If it's ok and nobody is working on that, I'll look on that subject.
I'll start by looking at the analize portion of vacuum. I'm thinking in
using arrays for the histogram (I've never used the array data type of
postgres).
Should I use 7.0.2 or the cvs version?
> Interesting article. We do most of what she talks about, but we don't
> have anything like the ClusterRatio statistic. We need it --- that was
> just being discussed a few days ago in another thread. Do you have any
> reference on exactly how DB2 defines that stat?
I don't remember seeing that information spefically. From what I've
read I can speculate:
1. They have clusterratios for both indexes and the relation itself.
2. They might use an index even if there is no "order by" if the table
has a low clusterratio: just to get the RIDs, then sort the RIDs and
fetch.
3. One possible way to calculate this ratio:
a) for tables
SeqScan
if tuple points to a next tuple on the same page then its
"good"
ratio = # good tuples / # all tuples
b) for indexes (high speculation ratio here)
foreach pointed RID in index
if RID is in same page of next RID in index than mark as
"good"
I suspect that if a tuple size is big (relative to page size) than the
cluster ratio is always low.
A tuple might also be "good" if it pointed to the next page.
Tiago
From pgsql-hackers-owner+M6152@hub.org Wed Aug 23 13:00:33 2000
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From: =?iso-8859-1?Q?Tiago_Ant=E3o?= <tra@fct.unl.pt>
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To: Tom Lane <tgl@sss.pgh.pa.us>
cc: Jules Bean <jules@jellybean.co.uk>, pgsql-hackers@postgresql.org
Subject: Re: [HACKERS] Optimisation deficiency: currval('seq')-->seq scan,
constant-->index scan
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Hi!
On Wed, 23 Aug 2000, Tom Lane wrote:
> Yes, we know about that one. We have stats about the most common value
> in a column, but no information about how the less-common values are
> distributed. We definitely need stats about several top values not just
> one, because this phenomenon of a badly skewed distribution is pretty
> common.
An end-biased histogram has stats on top values and also on the least
frequent values. So if a there is a selection on a value that is well
bellow average, the selectivity estimation will be more acurate. On some
research papers I've read, it's refered that this is a better approach
than equi-width histograms (which are said to be the "industry" standard).
I not sure whether to use a table or a array attribute on pg_stat for
the histogram, the problem is what could be expected from the size of the
attribute (being a text). I'm very affraid of the cost of going through
several tuples on a table (pg_histogram?) during the optimization phase.
One other idea would be to only have better statistics for special
attributes requested by the user... something like "analyze special
table(column)".
Best Regards,
Tiago
From pgsql-hackers-owner+M6160@hub.org Thu Aug 24 00:21:39 2000
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To: =?iso-8859-1?Q?Tiago_Ant=E3o?= <tra@fct.unl.pt>
cc: Jules Bean <jules@jellybean.co.uk>, pgsql-hackers@postgresql.org
Subject: Re: [HACKERS] Optimisation deficiency: currval('seq')-->seq scan, constant-->index scan
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Comments: In-reply-to =?iso-8859-1?Q?Tiago_Ant=E3o?= <tra@fct.unl.pt>
message dated "Wed, 23 Aug 2000 16:03:42 +0100"
Date: Wed, 23 Aug 2000 23:56:35 -0400
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From: Tom Lane <tgl@sss.pgh.pa.us>
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=?iso-8859-1?Q?Tiago_Ant=E3o?= <tra@fct.unl.pt> writes:
> One other idea would be to only have better statistics for special
> attributes requested by the user... something like "analyze special
> table(column)".
This might actually fall out "for free" from the cheapest way of
implementing the stats. We've talked before about scanning btree
indexes directly to obtain data values in sorted order, which makes
it very easy to find the most common values. If you do that, you
get good stats for exactly those columns that the user has created
indexes on. A tad indirect but I bet it'd be effective...
regards, tom lane
From pgsql-hackers-owner+M6165@hub.org Thu Aug 24 05:33:02 2000
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Date: Thu, 24 Aug 2000 10:11:14 +0100
From: Jules Bean <jules@jellybean.co.uk>
To: Tom Lane <tgl@sss.pgh.pa.us>
Cc: Tiago Ant?o <tra@fct.unl.pt>, pgsql-hackers@postgresql.org
Subject: Re: [HACKERS] Optimisation deficiency: currval('seq')-->seq scan, constant-->index scan
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On Wed, Aug 23, 2000 at 10:30:30AM -0400, Tom Lane wrote:
> Jules Bean <jules@jellybean.co.uk> writes:
> > I have in a table a 'category' column which takes a small number of
> > (basically fixed) values. Here by 'small', I mean ~1000, while the
> > table itself has ~10 000 000 rows. Some categories have many, many
> > more rows than others. In particular, there's one category which hits
> > over half the rows. Because of this (AIUI) postgresql assumes
> > that the query
> > select ... from thistable where category='something'
> > is best served by a seqscan, even though there is an index on
> > category.
>
> Yes, we know about that one. We have stats about the most common value
> in a column, but no information about how the less-common values are
> distributed. We definitely need stats about several top values not just
> one, because this phenomenon of a badly skewed distribution is pretty
> common.
ISTM that that might be enough, in fact.
If you have stats telling you that the most popular value is 'xyz',
and that it constitutes 50% of the rows (i.e. 5 000 000) then you can
conclude that, on average, other entries constitute a mere 5 000
000/999 ~~ 5000 entries, and it would be definitely be enough.
(That's assuming you store the number of distinct values somewhere).
> BTW, if your highly-popular value is actually a dummy value ('UNKNOWN'
> or something like that), a fairly effective workaround is to replace the
> dummy entries with NULL. The system does account for NULLs separately
> from real values, so you'd then get stats based on the most common
> non-dummy value.
I can't really do that. Even if I could, the distribution is very
skewed -- so the next most common makes up a very high proportion of
what's left. I forget the figures exactly.
Jules
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